A novel ensemble method for credit scoring: Adaption of different imbalance ratios. (15th May 2018)

• Record Type: Journal Article
• Authors: He, Hongliang; Zhang, Wenyu; Zhang, Shuai
• Is Part Of: Expert systems with applications. Volume 98(2018)
• Page Start: 105
• Page End: 117
• Publication Date: 2018-05-15
• Subjects: Credit scoring -- Ensemble model -- BalanceCascade -- Imbalance ratios; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.01.012
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel ensemble model adapting to different imbalance ratios is proposed. The BalanceCascade approach is extended to generate adjustable balanced sets. Random forest and extreme gradient boosting are used as base classifiers. Parameters of base classifiers are optimized with particle swarm optimization. The performance of the proposed model is superior to other comparative models. Abstract: In the past few decades, credit scoring has become an increasing concern for financial institutions and is currently a popular topic of research. This study aims to generate a novel ensemble model for credit scoring, to obtain superior performance and high robustness, adapting to different imbalance ratio datasets. First, according to the credit scoring data characteristics, the proposed model extends the BalanceCascade approach to generate adjustable balanced subsets based on the imbalance ratios of training data. Further, it reduces the negative effect of imbalanced data and improves the comprehensive performance of the predictive model. Second, the proposed model adopts two kinds of tree-based classifiers, random forest and extreme gradient boosting, as the base classifiers for a three-stage ensemble model. This includes the use of stacking to generate predicted results of the former layer as new explanatory features in the latter layer, and the use of a particle swarm optimization algorithm for parameters optimization of the base classifiers. Finally, the results indicate that the average performance of the proposed model is superior to other comparative algorithms as reflected in most evaluation measures for different datasets. It demonstrates that the proposed model is robust and represents a positive development in credit scoring.